Critical gaps across all dimensions. Tool names lack clear action verbs and are overly verbose (search_aralia_data_first_step, search_aralia_data_second_step, etc.). Descriptions are present but generic and lack actionable guidance on when/why to use each tool. Input schemas visible but severely underspecified: parameters lack type constraints, enums, ranges, or validation rules. No output schemas documented. No error handling guidance. Tools appear to form a rigid multi-step pipeline rather than composable, independent operations. No evidence of idempotency, pagination, or result limiting.

• Rename tools to action-focused names: search_datasets (replaces first_step), get_column_metadata (replaces second_step), get_filter_options (replaces third_step), fetch_chart_data (replaces final_step). Each name should stand alone and be callable independently.
• Document complete input and output schemas in JSON Schema format. For 'datasets' and 'charts' parameters, specify the required object structure (e.g., {id: string, name: string, sourceURL: string}). For outputs, define the exact response shape with field types and descriptions.
• Add parameter constraints: 'question' should have maxLength (e.g., 500 chars), 'datasets' and 'charts' should specify min/max array length and object schema. Include examples of valid input in descriptions, not as free text but as schema constraints.
• Decompose the pipeline into independent tools. Allow agents to call search_datasets alone, or chain it with get_column_metadata only if needed. Return all necessary IDs (dataset_id, sourceURL, column_id) in each response to enable flexible chaining.
• Add error handling guidance: 'If no datasets found, try a simpler query or check the keyword spelling.' Include per-tool timeout guidance and retry logic. Document which errors are retryable (network) vs user-fixable (invalid query) vs fatal (auth failure).